Path to a free self-taught education in Computer Science!
- About
- Becoming an OSS student
- Motivation & Preparation
- Curriculum
- How to use this guide
- Prerequisite
- How to collaborate
- Community
- Next Goals
- References
This is a solid path for those of you who want to complete a Computer Science course on your own time, for free, with courses from the best universities in the World.
In our curriculum, we gave preference to MOOC (Massive Open Online Course) style courses because those courses were created with our style of learning in mind.
To officially register for this course you must create a profile in our students profile issue.
"How can I do this?"
Comment in this issue (please, do not open a new one) using the following template:
- **Name**: YOUR NAME
- **GitHub**: [@your_username]()
- **Twitter**: [@your_username]()
- **Linkedin**: [link]()
- **Website**: [yourblog.com]()
## Completed Courses
**Name of the Section**
Course|Files
:--|:--:
Course Name| [link]()
IMPORTANT: add your profile only once and after you finish each course you can return to that issue and update your comment.
ps: In the Completed Courses section, you should link the repository that contains the files that you created in the respective course.
"Why should I do this?"
This is a way to get to know our peers better, and an opportunity to share the things that we have done.
That is why we are using this strategy. You are free to bypass this if you're not that type.
Here are two interesting links that can make all the difference in your journey.
The first one is a motivational video that shows a guy that went through the "MIT Challenge", that consists in learning the entire 4-year MIT curriculum for Computer Science in 1 year.
The second link is a MOOC that will teach you learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. These are fundamental abilities to succeed in our journey.
Are you ready to get started?
- Introduction to Computer Science
- Math (Mathematical Thinking)
- Program Design
- Math (Discrete Math)
- Algorithms
- Programming Paradigms
- Software Testing
- Math (Calculus)
- Software Architecture
- Theory
- Software Engineering
- Math (Probability)
- Computer Architecture
- Operating Systems
- Computer Networks
- Databases
- Cloud Computing
- Math (Linear Algebra)
- Cryptography
- Security
- Compilers
- Parallel Computing
- UX Design
- Computer Graphics
- Artificial Intelligence
- Machine Learning
- Natural Language Processing
- Big Data
- Data Mining
- Internet of Things
- Specializations
| Courses | Duration | Effort |
|---|---|---|
| Introduction to Computer Science | 12 weeks | 10-20 hours/week |
| Introduction to Computer Science and Programming Using Python | 9 weeks | 15 hours/week |
| Introduction to Computational Thinking and Data Science | 10 weeks | 15 hours/week |
| From Nand to Tetris | 7 weeks | 5-10 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Effective Thinking Through Mathematics | 9 weeks | 5 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Systematic Program Design- Part 1: The Core Method | 5 weeks | 8-12 hours/week |
| Systematic Program Design- Part 2: Arbitrary Sized Data | 5 weeks | 8-12 hours/week |
| Systematic Program Design- Part 3: Abstraction, Search and Graphs | 5 weeks | 8-12 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Mathematics for Computer Science | 12 weeks | 5 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Algorithms, Part I | 6 weeks | 6-12 hours/week |
| Algorithms, Part II | 6 weeks | 6-12 hours/week |
| Analysis of Algorithms | 6 weeks | 6-8 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Functional Programming Principles in Scala | 7 weeks | 5-7 hours/week |
| Principles of Reactive Programming | 7 weeks | 5-7 hours/week |
| Object Oriented Programming in Java | 6 weeks | 4-6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Software Testing | 4 weeks | 6 hours/week |
| Software Debugging | 8 weeks | 6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Calculus One | 16 weeks | 8-10 hours/week |
| Calculus Two: Sequences and Series | 7 weeks | 9-10 hours/week |
| Multivariable Calculus | 6 weeks | 5-7 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Web Application Architectures | 6 weeks | 6-9 hours/week |
| Software Architecture & Design | 8 weeks | 6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Automata | 6 weeks | 8-10 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Engineering Software as a Service (SaaS), Part 1 | 9 weeks | 12 hours/week |
| Engineering Software as a Service (Saas), Part 2 | 8 weeks | 12 hours/week |
| Software Processes and Agile Practices | 4 weeks | 6-8 hours/week |
| Startup Engineering | 12 weeks | 2-20 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Introduction to Probability - The Science of Uncertainty | 16 weeks | 12 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| The Hardware/Software Interface | 8 weeks | 10-15 hours/week |
| Computer Architecture | - | 5-8 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Operating System Engineering | - | - |
| Operating Systems and System Programming | 10 weeks | 2-3 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Introduction to Computer Networking | - | 5-10 hours/week |
| Computer Networks | - | 4–12 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Databases | 12 weeks | 8-12 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Introduction to Cloud Computing | 4 weeks | 1 hour/week |
| Courses | Duration | Effort |
|---|---|---|
| Coding the Matrix: Linear Algebra through Computer Science Applications | 10 weeks | 7-10 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Cryptography I | 6 weeks | 5-7 hours/week |
| Cryptography II | 6 weeks | 6-8 hours/week |
| Applied Cryptography | 8 weeks | 6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Introduction to Cyber Security | 8 weeks | 3 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Compilers | 9 weeks | 6-8 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Heterogeneous Parallel Programming | 11 weeks | 8-10 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| UX Design for Mobile Developers | 6 weeks | 6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Computer Graphics | 6 weeks | 12 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Artificial Intelligence | 12 weeks | 15 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Machine Learning | 11 weeks | 4-6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Natural Language Processing | 10 weeks | 8-10 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Introduction to Big Data | 3 weeks | 5-6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Pattern Discovery in Data Mining | 4 weeks | 4-6 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| The Internet of Things | 4 weeks | 2 hours/week |
After finishing the courses above, start your specializations on the topics that you have more interest.
Search such specializations in the following platforms:
Coursera | edX | Udacity | Future Learn | Udemy
This guide was developed to be consumed in a linear approach. What does this mean? That you should complete one course at a time.
The courses are already in the order that you should complete them. Just start in the Introduction to Computer Science section and after finishing the first course, start the next one.
If the course isn't open, do it anyway with the resources from the previous class.
Yes! The intention is to conclude all the courses listed here!
It may take longer to complete all of the classes compared to a regular CS course, but I can guarantee you that your reward will be proportional to your motivation/dedication!
You must focus on your habit, and forget about goals. Try to invest 1 ~ 2 hours every day studying this curriculum. If you do this, inevitably you'll finish this curriculum.
See more about "Commit to a process, not a goal" here.
Here in OSS University, you do not need to take exams, because we are focused on real projects!
In order to show for everyone that you successfully finished a course, you should create a "startup project".
"What does it mean?"
After finish a course, you should think about a real world problem that you can solve using the acquired knowledge in the course. You don't need to create a big project, but you must create something to validate and consolidate your knowledge, and also to show to the world that you are capable to create something useful with the concepts that you learned.
The projects of all students will be listed in this file. Submit your project's information in that file after you conclude it.
You can create this project alone or with other students!
And you should also...
This is a crucial part of your journey through all those courses.
You need to have in mind that what you are able to create with the concepts that you learned will be your certificate and this is what really matters!
In order to show that you really learned those things, you need to be creative!
Here are some tips about how you can do that:
- Articles: create blog posts to synthesize/summarize what you learned.
- GitHub repository: keep your course's files organized in a GH repository, so in that way other students can use it to study with your annotations.
We love cooperative work! Use our channels to communicate with other fellows to combine and create new projects!
My friend, here is the best part of liberty! You can use any language that you want to complete the courses.
The important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.
You must share only files that you are allowed to! Do NOT disrespect the code of conduct that you signed in the beginning of some courses.
Be creative in order to show your progress! 😄
Watch this repository for futures improvements and general information.
The only things that you need to know are how to use Git and GitHub. Here are some resources to learn about them:
Note: Just pick one of the courses below to learn the basics. You will learn a lot more once you get started!
- Try Git
- [Git - the simple guide] (http://rogerdudler.github.io/git-guide/)
- GitHub Training & Guides
- GitHub Hello World
- Git Immersion
- How to Use Git and GitHub
Curriculum Version: 1.3.7
To show respect to all of our students, we will keep a CHANGELOG file that contains all the alterations that our curriculum may suffer.
Now we have a stable version of the curriculum, which won't change anymore, only in exceptional cases (outdated courses, broken links, etc).
Our students can trust in this curriculum because it has been carefully planned and covers all the core topics that a conventional Computer Science course covers.
We also include modern topics, making this course one of the best options for those who want to become a Computer Scientist and/or a Software Engineer.
You can open an issue and give us your suggestions as to how we can improve this guide, or what we can do to improve the learning experience.
You can also fork this project to fix any mistakes that you have found or add new things, and then make a pull request.
Let's do it together! =)
Subscribe to /r/opensourcesociety!
Join us in our group!
You can also interact through GitHub issues.
We also have a chat room!
Add Open Source Society University to your Facebook profile!
ps: A forum is an ideal way to interact with other students as we do not lose important discussions, which usually occur in communication via chat apps. Please use our subreddit/group for important discussions.
- Add our University page at Linkedin, so in that way we will be able to add OSS University in our Linkedin profile.